A continuous distribution in which the logarithm of a variable has a normal distribution. A log normal
distribution results if the variable is the product of a large number of independent,
identically-distributed variables in the same way that a normal distribution results if the variable is the
sum of a large number of independent, identically-distributed variables.

In probability theory and statistics, the cumulative distribution function (CDF), or just distribution
function, describes the probability that a real-valued random variable X with a given probability
distribution will be found at a value less than or equal to x.

In probability theory, a probability density function (pdf), or density of a continuous random variable
is a function that describes the relative likelihood for this random variable to occur at a given
point.

make

getDistribution

In probability theory and statistics, the cumulative distribution function (CDF), or just distribution
function, describes the probability that a real-valued random variable X with a given probability
distribution will be found at a value less than or equal to x. Intuitively, it is the "area so far"
function of the probability distribution. Cumulative distribution functions are also used to specify
the distribution of multivariate random variables.
WikipediA

Parameters:

value - x

Returns:

P(<=x)

getExpected

public double getExpected()

getGeometricMean

public double getGeometricMean()

The geometric mean is also the median

getGeometricStandardDeviation

public double getGeometricStandardDeviation()

getProbability

In probability theory, a probability density function (pdf), or density of a continuous random variable
is a function that describes the relative likelihood for this random variable to occur at a given
point. The probability for the random variable to fall within a particular region is given by the
integral of this variable's density over the region. The probability density function is nonnegative
everywhere, and its integral over the entire space is equal to one.
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Parameters:

value - x

Returns:

P(x)

getQuantile

The quantile function, for any distribution, is defined for real variables between zero and one and is
mathematically the inverse of the cumulative distribution function.
WikipediA The input probability absolutely
has to be [0.0, 1.0], but values close to 0.0 and 1.0 may be problematic